OpenRaiser/Intern-Atlas huggingface dataset:
Brazil Data Commons: A Platform for Unifying and Integrating Brazil's Public Data
Venue: arXiv cs.CY 2025
Year: 2025

The fragmentation of public data in Brazil, coupled with inconsistent standards and limited interoperability, hinders effective research, evidence-based policymaking and access to data-driven insights. To address these issues, we introduce Brazil Data Commons, a platform that unifies various Brazilian datasets under a common semantic framework, enabling the seamless discovery, integration and visualization of information from different domains. By adopting globally recognized ontologies and interoperable data standards, Brazil Data Commons aligns with the principles of the broader Data Commons ecosystem and places Brazilian data in a global context. Through user-friendly interfaces, straightforward query mechanisms and flexible data access options, the platform democratizes data use and enables researchers, policy makers, and the public to gain meaningful insights and make informed decisions. This paper illustrates how Brazil Data Commons transforms scattered datasets into an integrated and easily navigable resource that allows a deeper understanding of Brazil's complex social, economic and environmental landscape.
MALBO: Optimizing LLM-Based Multi-Agent Teams via Multi-Objective Bayesian Optimization
Venue: arXiv cs.MA 2025
Year: 2025

The optimal assignment of Large Language Models (LLMs) to specialized roles in multi-agent systems is a significant challenge, defined by a vast combinatorial search space, expensive black-box evaluations, and an inherent trade-off between performance and cost. Current optimization methods focus on single-agent settings and lack a principled framework for this multi-agent, multi-objective problem. This thesis introduces MALBO (Multi-Agent LLM Bayesian Optimization), a systematic framework designed to automate the efficient composition of LLM-based agent teams. We formalize the assignment challenge as a multi-objective optimization problem, aiming to identify the Pareto front of configurations between task accuracy and inference cost. The methodology employs multi-objective Bayesian Optimization (MOBO) with independent Gaussian Process surrogate models. By searching over a continuous feature-space representation of the LLMs, this approach performs a sample-efficient exploration guided by the expected hypervolume improvement. The primary contribution is a principled and automated methodology that yields a Pareto front of optimal team configurations. Our results demonstrate that the Bayesian optimization phase, compared to an initial random search, maintained a comparable average performance while reducing the average configuration cost by over 45%. Furthermore, MALBO identified specialized, heterogeneous teams that achieve cost reductions of up to 65.8% compared to homogeneous baselines, all while maintaining maximum performance. The framework thus provides a data-driven tool for deploying cost-effective and highly specialized multi-agent AI systems.
Lessons Learned from Developing a Privacy-Preserving Multimodal Wearable for Local Voice-and-Vision Inference
Venue: arXiv cs.HC 2025
Year: 2025

Many promising applications of multimodal wearables require continuous sensing and heavy computation, yet users reject such devices due to privacy concerns. This paper shares our experiences building an ear-mounted voice-and-vision wearable that performs local AI inference using a paired smartphone as a trusted personal edge. We describe the hardware-software co-design of this privacy-preserving system, including challenges in integrating a camera, microphone, and speaker within a 30-gram form factor, enabling wake word-triggered capture, and running quantized vision-language and large-language models entirely offline. Through iterative prototyping, we identify key design hurdles in power budgeting, connectivity, latency, and social acceptability. Our initial evaluation shows that fully local multimodal inference is feasible on commodity mobile hardware with interactive latency. We conclude with design lessons for researchers developing embedded AI systems that balance privacy, responsiveness, and usability in everyday settings.
Scaling Open-Weight Large Language Models for Hydropower Regulatory Information Extraction: A Systematic Analysis
Venue: arXiv cs.CL 2025
Year: 2025

Information extraction from regulatory documents using large language models presents critical trade-offs between performance and computational resources. We evaluated seven open-weight models (0.6B-70B parameters) on hydropower licensing documentation to provide empirical deployment guidance. Our analysis identified a pronounced 14B parameter threshold where validation methods transition from ineffective (F1 $<$ 0.15) to viable (F1 = 0.64). Consumer-deployable models achieve 64\% F1 through appropriate validation, while smaller models plateau at 51\%. Large-scale models approach 77\% F1 but require enterprise infrastructure. We identified systematic hallucination patterns where perfect recall indicates extraction failure rather than success in smaller models. Our findings establish the first comprehensive resource-performance mapping for open-weight information extraction in regulatory contexts, enabling evidence-based model selection. These results provide immediate value for hydropower compliance while contributing insights into parameter scaling effects that generalize across information extraction tasks.
Real-Time Speech Enhancement via a Hybrid ViT: A Dual-Input Acoustic-Image Feature Fusion
Venue: arXiv cs.SD 2025
Year: 2025

Speech quality and intelligibility are significantly degraded in noisy environments. This paper presents a novel transformer-based learning framework to address the single-channel noise suppression problem for real-time applications. Although existing deep learning networks have shown remarkable improvements in handling stationary noise, their performance often diminishes in real-world environments characterized by non-stationary noise (e.g., dog barking, baby crying). The proposed dual-input acoustic-image feature fusion using a hybrid ViT framework effectively models both temporal and spectral dependencies in noisy signals. Designed for real-world audio environments, the proposed framework is computationally lightweight and suitable for implementation on embedded devices. To evaluate its effectiveness, four standard and commonly used quality measurements, namely PESQ, STOI, Seg SNR, and LLR, are utilized. Experimental results obtained using the Librispeech dataset as the clean speech source and the UrbanSound8K and Google Audioset datasets as the noise sources, demonstrate that the proposed method significantly improves noise reduction, speech intelligibility, and perceptual quality compared to the noisy input signal, achieving performance close to the clean reference.
Conformal Constrained Policy Optimization for Cost-Effective LLM Agents
Venue: arXiv cs.LG 2025
Year: 2025

While large language models (LLMs) have recently made tremendous progress towards solving challenging AI problems, they have done so at increasingly steep computational and API costs. We propose a novel strategy where we combine multiple LLM models with varying cost/accuracy tradeoffs in an agentic manner, where models and tools are run in sequence as determined by an orchestration model to minimize cost subject to a user-specified level of reliability; this constraint is formalized using conformal prediction to provide guarantees. To solve this problem, we propose Conformal Constrained Policy Optimization (CCPO), a training paradigm that integrates constrained policy optimization with off-policy reinforcement learning and recent advances in online conformal prediction. CCPO jointly optimizes a cost-aware policy (score function) and an adaptive threshold. Across two multi-hop question answering benchmarks, CCPO achieves up to a 30% cost reduction compared to other cost-aware baselines and LLM-guided methods without compromising reliability. Our approach provides a principled and practical framework for deploying LLM agents that are significantly more cost-effective while maintaining reliability.
MP-GFormer: A 3D-Geometry-Aware Dynamic Graph Transformer Approach for Machining Process Planning
Venue: arXiv cs.CV 2025
Year: 2025

Machining process planning (MP) is inherently complex due to structural and geometrical dependencies among part features and machining operations. A key challenge lies in capturing dynamic interdependencies that evolve with distinct part geometries as operations are performed. Machine learning has been applied to address challenges in MP, such as operation selection and machining sequence prediction. Dynamic graph learning (DGL) has been widely used to model dynamic systems, thanks to its ability to integrate spatio-temporal relationships. However, in MP, while existing DGL approaches can capture these dependencies, they fail to incorporate three-dimensional (3D) geometric information of parts and thus lack domain awareness in predicting machining operation sequences. To address this limitation, we propose MP-GFormer, a 3D-geometry-aware dynamic graph transformer that integrates evolving 3D geometric representations into DGL through an attention mechanism to predict machining operation sequences. Our approach leverages StereoLithography surface meshes representing the 3D geometry of a part after each machining operation, with the boundary representation method used for the initial 3D designs. We evaluate MP-GFormer on a synthesized dataset and demonstrate that the method achieves improvements of 24\% and 36\% in accuracy for main and sub-operation predictions, respectively, compared to state-of-the-art approaches.
Evolution of A4L: A Data Architecture for AI-Augmented Learning
Venue: arXiv cs.ET 2025
Year: 2025

As artificial intelligence (AI) becomes more deeply integrated into educational ecosystems, the demand for scalable solutions that enable personalized learning continues to grow. These architectures must support continuous data flows that power personalized learning and access to meaningful insights to advance learner success at scale. At the National AI Institute for Adult Learning and Online Education (AI-ALOE), we have developed an Architecture for AI-Augmented Learning (A4L) to support analysis and personalization of online education for adult learners. A4L1.0, an early implementation by Georgia Tech's Design Intelligence Laboratory, demonstrated how the architecture supports analysis of meso- and micro-learning by integrating data from Learning Management Systems (LMS) and AI tools. These pilot studies informed the design of A4L2.0. In this chapter, we describe A4L2.0 that leverages 1EdTech Consortium's open standards such as Edu-API, Caliper Analytics, and Learning Tools Interoperability (LTI) to enable secure, interoperable data integration across data systems like Student Information Systems (SIS), LMS, and AI tools. The A4L2.0 data pipeline includes modules for data ingestion, preprocessing, organization, analytics, and visualization.
FLEX: Feature Importance from Layered Counterfactual Explanations
Venue: arXiv cs.LG 2025
Year: 2025

Machine learning models achieve state-of-the-art performance across domains, yet their lack of interpretability limits safe deployment in high-stakes settings. Counterfactual explanations are widely used to provide actionable "what-if" recourse, but they typically remain instance-specific and do not quantify which features systematically drive outcome changes within coherent regions of the feature space or across an entire dataset. We introduce FLEX (Feature importance from Layered counterfactual EXplanations), a model- and domain-agnostic framework that converts sets of counterfactuals into feature change frequency scores at local, regional, and global levels. FLEX generalises local change-frequency measures by aggregating across instances and neighbourhoods, offering interpretable rankings that reflect how often each feature must change to flip predictions. The framework is compatible with different counterfactual generation methods, allowing users to emphasise characteristics such as sparsity, feasibility, or actionability, thereby tailoring the derived feature importances to practical constraints. We evaluate FLEX on two contrasting tabular tasks: traffic accident severity prediction and loan approval, and compare FLEX to SHAP- and LIME-derived feature importance values. Results show that (i) FLEX's global rankings correlate with SHAP while surfacing additional drivers, and (ii) regional analyses reveal context-specific factors that global summaries miss. FLEX thus bridges the gap between local recourse and global attribution, supporting transparent and intervention-oriented decision-making in risk-sensitive applications.
Enhancing XR Auditory Realism via Multimodal Scene-Aware Acoustic Rendering
Venue: arXiv cs.HC 2025
Year: 2025

In Extended Reality (XR), rendering sound that accurately simulates real-world acoustics is pivotal in creating lifelike and believable virtual experiences. However, existing XR spatial audio rendering methods often struggle with real-time adaptation to diverse physical scenes, causing a sensory mismatch between visual and auditory cues that disrupts user immersion. To address this, we introduce SAMOSA, a novel on-device system that renders spatially accurate sound by dynamically adapting to its physical environment. SAMOSA leverages a synergistic multimodal scene representation by fusing real-time estimations of room geometry, surface materials, and semantic-driven acoustic context. This rich representation then enables efficient acoustic calibration via scene priors, allowing the system to synthesize a highly realistic Room Impulse Response (RIR). We validate our system through technical evaluation using acoustic metrics for RIR synthesis across various room configurations and sound types, alongside an expert evaluation (N=12). Evaluation results demonstrate SAMOSA's feasibility and efficacy in enhancing XR auditory realism.
InData: Towards Secure Multi-Step, Tool-Based Data Analysis
Venue: arXiv cs.CL 2025
Year: 2025

Large language model agents for data analysis typically generate and execute code directly on databases. However, when applied to sensitive data, this approach poses significant security risks. To address this issue, we propose a security-motivated alternative: restrict LLMs from direct code generation and data access, and require them to interact with data exclusively through a predefined set of secure, verified tools. Although recent tool-use benchmarks exist, they primarily target tool selection and simple execution rather than the compositional, multi-step reasoning needed for complex data analysis. To reduce this gap, we introduce Indirect Data Engagement (InData), a dataset designed to assess LLMs' multi-step tool-based reasoning ability. InData includes data analysis questions at three difficulty levels--Easy, Medium, and Hard--capturing increasing reasoning complexity. We benchmark 15 open-source LLMs on InData and find that while large models (e.g., gpt-oss-120b) achieve high accuracy on Easy tasks (97.3%), performance drops sharply on Hard tasks (69.6%). These results show that current LLMs still lack robust multi-step tool-based reasoning ability. With InData, we take a step toward enabling the development and evaluation of LLMs with stronger multi-step tool-use capabilities. We will publicly release the dataset and code.
From Events to Clarity: The Event-Guided Diffusion Framework for Dehazing
Venue: arXiv cs.CV 2025
Year: 2025

Clear imaging under hazy conditions is a critical task. Prior-based and neural methods have improved results. However, they operate on RGB frames, which suffer from limited dynamic range. Therefore, dehazing remains ill-posed and can erase structure and illumination details. To address this, we use event cameras for dehazing for the \textbf{first time}. Event cameras offer much higher HDR ($120 dBvs.60 dB$) and microsecond latency, therefore they suit hazy scenes. In practice, transferring HDR cues from events to frames is hard because real paired data are scarce. To tackle this, we propose an event-guided diffusion model that utilizes the strong generative priors of diffusion models to reconstruct clear images from hazy inputs by effectively transferring HDR information from events. Specifically, we design an event-guided module that maps sparse HDR event features, \textit{e.g.,} edges, corners, into the diffusion latent space. This clear conditioning provides precise structural guidance during generation, improves visual realism, and reduces semantic drift. For real-world evaluation, we collect a drone dataset in heavy haze (AQI = 341) with synchronized RGB and event sensors. Experiments on two benchmarks and our dataset achieve state-of-the-art results.
Computation-aware Energy-harvesting Federated Learning: Cyclic Scheduling with Selective Participation
Venue: arXiv cs.LG 2025
Year: 2025

Federated Learning (FL) is a powerful paradigm for distributed learning, but its increasing complexity leads to significant energy consumption from client-side computations for training models. In particular, the challenge is critical in energy-harvesting FL (EHFL) systems where participation availability of each device oscillates due to limited energy. To address this, we propose FedBacys, a battery-aware EHFL framework using cyclic client participation based on users' battery levels. By clustering clients and scheduling them sequentially, FedBacys minimizes redundant computations, reduces system-wide energy usage, and improves learning stability. We also introduce FedBacys-Odd, a more energy-efficient variant that allows clients to participate selectively, further reducing energy costs without compromising performance. We provide a convergence analysis for our framework and demonstrate its superior energy efficiency and robustness compared to existing algorithms through numerical experiments.
Educators on the Frontline: Philosophical and Realistic Perspectives on Integrating ChatGPT into the Learning Space
Venue: arXiv cs.CY 2025
Year: 2025

The rapid emergence of Generative AI, particularly ChatGPT, has sparked a global debate on the future of education, often characterized by alarmism and speculation. Moving beyond this, this study investigates the structured, grounded perspectives of a key stakeholder group: university educators. It proposes a novel theoretical model that conceptualizes the educational environment as a "Learning Space" composed of seven subspaces to systematically identify the impact of AI integration. This framework was operationalized through a quantitative survey of 140 Russian university educators, with responses analyzed using a binary flagging system to measure acceptance across key indicators. The results reveal a strong but conditional consensus: a majority of educators support ChatGPT's integration, contingent upon crucial factors such as the transformation of assessment methods and the availability of plagiarism detection tools. However, significant concerns persist regarding its impact on critical thinking. Educators largely reject the notion that AI diminishes their importance, viewing their role as evolving from information-deliverer to facilitator of critical engagement. The study concludes that ChatGPT acts less as a destroyer of education and more as a catalyst for its necessary evolution, and proposes the PIPE Model (Pedagogy, Infrastructure, Policy, Education) as a strategic framework for its responsible integration. This research provides a data-driven, model-based analysis of educator attitudes, offering a nuanced alternative to the polarized discourse surrounding AI in education.
Quantile Q-Learning: Revisiting Offline Extreme Q-Learning with Quantile Regression
Venue: arXiv cs.LG 2025
Year: 2025

Offline reinforcement learning (RL) enables policy learning from fixed datasets without further environment interaction, making it particularly valuable in high-risk or costly domains. Extreme $Q$-Learning (XQL) is a recent offline RL method that models Bellman errors using the Extreme Value Theorem, yielding strong empirical performance. However, XQL and its stabilized variant MXQL suffer from notable limitations: both require extensive hyperparameter tuning specific to each dataset and domain, and also exhibit instability during training. To address these issues, we proposed a principled method to estimate the temperature coefficient $β$ via quantile regression under mild assumptions. To further improve training stability, we introduce a value regularization technique with mild generalization, inspired by recent advances in constrained value learning. Experimental results demonstrate that the proposed algorithm achieves competitive or superior performance across a range of benchmark tasks, including D4RL and NeoRL2, while maintaining stable training dynamics and using a consistent set of hyperparameters across all datasets and domains.
A Logspace Constructive Proof of L=SL
Venue: arXiv cs.LO 2025
Year: 2025

We formalize the proof of Reingold's Theorem that SL=L [Rei05] in the theory of bounded arithmetic VL, which corresponds to ``logspace reasoning''. As a consequence, we get that VL=VSL, where VSL is the theory of bounded arithmetic for ``symmetric-logspace reasoning''. This resolves in the affirmative an old open question from Kolokolova [Kol05] (see also Cook-Nguyen [NC10]). Our proof relies on the Rozenman-Vadhan alternative proof of Reingold's Theorem ([RV05]). To formalize this proof in VL, we need to avoid reasoning about eigenvalues and eigenvectors (common in both original proofs of SL=L). We achieve this by using some results from Buss-Kabanets-Kolokolova-Koucký [Bus+20] that allow VL to reason about graph expansion in combinatorial terms.
A Quick and Exact Method for Distributed Quantile Computation
Venue: arXiv cs.DC 2025
Year: 2025

Quantile computation is a core primitive in large-scale data analytics. In Spark, practitioners typically rely on the Greenwald-Khanna (GK) Sketch, an approximate method. When exact quantiles are required, the default option is an expensive global sort. We present GK Select, an exact Spark algorithm that avoids full-data shuffles and completes in a constant number of actions. GK Select leverages GK Sketch to identify a near-target pivot, extracts all values within the error bound around this pivot in each partition in linear time, and then tree-reduces the resulting candidate sets. We show analytically that GK Select matches the executor-side time complexity of GK Sketch while returning the exact quantile. Empirically, GK Select achieves sketch-level latency and outperforms Spark's full sort by approximately 10.5x on 10^9 values across 120 partitions on a 30-core AWS EMR cluster.